It is well known that various physical phenomena can be detected by sensor systems which selectively use electrical, mechanical or chemical sensors. It happens, however, that each physical phenomenon, or event, in a particular physical environment will have not just one, but many types of physical characteristics that distinguish it from other phenomena which may occur in the environment. Thus, not only might an environment have many different simultaneously occurring events, each possible event in the environment has many different types of physical characteristics, with each characteristic being measurable by a different type of sensor. For a typical known sensor system, however, the particular phenomenon, or event, to be detected is identified by using only one sensor. Consequently, only one salient characteristic of the event can be identified. Furthermore, for this single characteristic of the event, only one response signal is normally generated. Unfortunately, the response signal may be inaccurate or even incorrect.
A simple smoke detector/fire alarm is an example of a typical case where an incorrect response may easily, and often does, result. As we know, smoke can sometimes be present even though there is no actual threat of fire. If smoke is present, however, the smoke detector will sense smoke and, depending on the setpoint of the detector, the fire alarm will be activated. This can be a false alarm. For many reasons which need not be enumerated here, false alarms are unwanted and are to be avoided, not only in fire alarm systems, but in other types of sensor systems as well.
Another example of an application in which a single sensor will not produce a reliable indication of a physical event is the monitoring of gases in electrical transformers. Many of these transformers are filled with oil for the purpose of cooling and insulation. As a transformer ages, and as it is subjected to high loads, varying loads, and severe environmental conditions, various components within the transformer will necessarily begin to degrade or eventually to fail. In addition, some defect or misuse can cause failure of some components of the transformer. As components degrade or fail, or as other undesirable processes occur within the transformer, various chemicals can be created in or released into the oil in the transformer. These chemicals can be in the form of dissolved gases, or they can react with other chemicals to form dissolved gases in the transformer oil. A single gas sensor placed in the transformer oil could not possibly yield a reliable indication of the complex process which occurs when the degradation or failure of transformer components begins.
Therefore, it is well known to periodically sample transformer oil to analyze the dissolved gases to detect the aging or failure of various components, or to detect other processes that might take place within the transformer. The purpose of this analysis is to determine when maintenance, repair, or even replacement of the transformer is necessary. The existence in the transformer oil of a given gas in a given concentration might indicate failure of paper or some other insulator, or it might indicate electrical arcing between components, or it might simply indicate a harmless effect of normal operation. Detecting a single gas dissolved in the oil will seldom reveal a complete and accurate picture of what is happening in the transformer. In order to accurately identify the occurrence of a particular type of problem in the transformer, it is usually necessary to detect the presence and the concentrations of a number of known gases in the oil.
Currently, since a single sensor can not yield the necessary information, sampling and analysis of transformer oil is commonly done by sending personnel out to the transformer, drawing a sample of the oil, taking the sample to a laboratory, and running analysis by methods such as gas chromatography to detect the presence and concentrations of the dissolved gases in the oil. In view of the large number of transformers in service, this requires a very large investment in man hours and equipment. It also affects the load capacity of the distribution system involved, and it requires numerous personnel entries into substations and other hazardous areas. Further, information on the dissolved gases present in a given transformer can only be obtained at infrequent intervals, for reasons of economy. Currently known systems are too expensive to be permanently installed on a single transformer, and they would be inherently inaccurate because of an inability to correctly interpret the status of a transformer based on the information that would be available from currently known sensors, without the presence of an operator.
In general, one possibility for improving a simple one-sensor system is to use a high-quality sophisticated sensor. Specifically, some sensors are more stable and more reliable than other sensors, for long periods of time. Such sensors, although they may help reduce the occurrences of false alarms, can be costly. Furthermore, for many applications, a single sensor, even a high quality sensor, may be ineffective, because it cannot generate the proper data. A single sensor is limited in the number of characteristics it can detect, and this can make it ineffective for a given application, such as, for example, in the transformer monitoring application.
Another possibility for improving the quality and ability of a sensor system is to increase the number of sensors which are used to detect a given characteristic of a given physical event within the environment. To this end, using a large number of the same kind of sensors will increase redundancy in the sensor system and improve the system's reliability. Furthermore, a large number of sensors allow for the averaging of the sensors' outputs. This may improve the system's accuracy. Nevertheless, when all sensors in a system are the same type, the system is still limited to detection of the same characteristic that is detectable by any one of the sensors.
Still another possibility for improving a sensor system is to combine a larger number of various types and kinds of sensors together in a single system. There is a problem, however, which arises when various types and kinds of sensors are used together. This problem is to find a way to effectively combine all of the various outputs from the sensors to produce meaningful results. Clearly, different types of sensors will detect different characteristics of a given environmental event and, accordingly, will generate different output signals. The downside here is that when the output signals of the different types of sensors are collected together, the result is a convolved pattern of data that is extremely garbled.
The present invention recognizes that a convolved pattern of garbled data which is generated by various diverse types of sensors in a single sensor system can be extremely useful. Indeed, the convolved pattern of data can be used to identify and evaluate a particular type of environmental event. This possibility, however, exists only if the convolved pattern generated by the various sensor outputs is properly analyzed. Accordingly, the present invention recognizes that pattern recognition units, such as neural networks and fuzzy logic processors are useful for these purposes.
In light of the above, it is an object of the present invention to provide a device, and a method for its use, which generates a discrete signal from a convolved pattern of many types of sensor signals. Another object of the present invention is to provide a device, and a method for its use, that generates a convolved pattern of signals from the collective outputs of diverse types of sensors, each of which monitors a different characteristic of an environmental event. Yet another object of the present invention is to provide a reliable and robust sensor system which is relatively easy to manufacture and install, which is simple to use, and which is comparatively cost effective.